Goal Reasoning: Papers from the ACS Workshop

This technical report contains the 14 accepted papers presented at the Workshop on Goal Reasoning, which was held as part of the 2015 Conference on Advances in Cognitive Systems (ACS-15) in Atlanta, Georgia on 28 May 2015. This is the fourth in a series of workshops related to this topic, the first of which was the AAAI-10 Workshop on Goal-Directed Autonomy; the second was the Self-Motivated Agents (SeMoA) Workshop, held at Lehigh University in November 2012; and the third was the Goal Reasoning Workshop at ACS-13 in Baltimore, Maryland in December 2013

Quantifying the effects of temperature on mosquito and parasite traits that determine the transmission potential of human malaria

Malaria transmission is known to be strongly impacted by temperature. The current understanding of how temperature affects mosquito and parasite life history traits derives from a limited number of empirical studies. These studies, some dating back to the early part of last century, are often poorly controlled, have limited replication, explore a narrow range of temperatures, and use a mixture of parasite and mosquito species. Here, we use a single pairing of the Asian mosquito vector, An. stephensi and the human malaria parasite, P. falciparum to conduct a comprehensive evaluation of the thermal performance curves of a range of mosquito and parasite traits relevant to transmission. We show that biting rate, adult mortality rate, parasite development rate, and vector competence are temperature sensitive. Importantly, we find qualitative and quantitative differences to the assumed temperature-dependent relationships. To explore the overall implications of temperature for transmission, we first use a standard model of relative vectorial capacity. This approach suggests a temperature optimum for transmission of 29°C, with minimum and maximum temperatures of 12°C and 38°C, respectively. However, the robustness of the vectorial capacity approach is challenged by the fact that the empirical data violate several of the model's simplifying assumptions. Accordingly, we present an alternative model of relative force of infection that better captures the observed biology of the vector-parasite interaction. This model suggests a temperature optimum for transmission of 26°C, with a minimum and maximum of 17°C and 35°C, respectively. The differences between the models lead to potentially divergent predictions for the potential impacts of current and future climate change on malaria transmission. The study provides a framework for more detailed, system-specific studies that are essential to develop an improved understanding on the effects of temperature on malaria transmission

Understanding What May Have Happened in Dynamic, Partially Observable Environments

In this work, we address the problem of understanding what may have happened in a goal-based deliberative agent's environment after the occurrence of exogenous actions and events. Such an agent observes, periodically, information about the state of the world, but this information is incomplete, and reasons for state changes are not observed. We propose methods a goal-based agent can use to construct internal, causal explanations of its observations based on a model of its environment. These explanations comprise a series of inferred actions and events that have occurred and continue to occur in its world, as well as assumptions about the initial state of the world. We show that an agent can more accurately predict future events and states by reference to these explanations, and thereby more reliably achieve its goals. This dissertation presents the following novel contributions: (1) a formalization of the problems of achieving goals, understanding what has happened, and updating an agent's model in a partially observable, dynamic world with partially known dynamics; (2) a complete agent (DHAGENT) that achieves goals in such environments more reliably than existing agents; (3) a novel algorithm (DISCOVERHISTORY) and technique (DISCOVER HISTORY search) for rapidly and accurately iteratively constructing causal explanations of what may have happened in these environments; (4) an examination of formal properties of these techniques; (5) a novel method (EML), capable of inferring improved models of an environment based on a small number of training scenarios; (6) experiments supporting performance claims about the novel methods described; and (7) an analysis of the efficiency of two DISCOVERHISTORY algorithm implementations

A Testbed for Evaluating AI Research Systems in Commercial Games

Many AI researchers want to test the utility of their prototypes in complex task environments, such as those defined by commercial gaming simulators. Also, many developers of commercial games need to solve tasks (e.g., game balancing, providing rational agent behaviors) that can be addressed by these systems. However, integrating them with gaming simulators requires substantial effort. We will demonstrate TIELT, a testbed designed to assist with evaluating research prototypes in these task environments

Aha, D.W., & Molineaux, M. (2004). Integrating learning in interactive gaming simulators Challenges in Game AI: Papers of the AAAI’04 Workshop (Technical Report WS-04-04). San José, CA: AAAI Press. Integrating Learning in Interactive Gaming Simulators

Many developers of simulations for computer-generated forces and real-time strategy games seek to incorporate learning or learned behaviors in their systems. Likewise, many researchers seek to evaluate their learning systems in these simulators. However, these integrations require great effort. We describe our initial work on a testbed, named TIELT that we are designing to facilitate these integrations